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BackgroundChikungunya virus (CHIKV) has spread across Brazil with varying incidence rates depending on the affected areas. Due to cocirculation of arboviruses and overlapping disease symptoms, CHIKV infection may be underdiagnosed. To understand the lack of CHIKV epidemics in São José do Rio Preto (SJdRP), São Paulo (SP), Brazil, we evaluated viral circulation by investigating anti-CHIKV IgG seroconversion in a prospective study of asymptomatic individuals and detecting anti-CHIKV IgM in individuals suspected of dengue infection, as well as CHIKV presence in Aedes mosquitoes. The opportunity to assess two different groups (symptomatic and asymptomatic) exposed at the same geographic region aimed to broaden the possibility of identifying the viral circulation, which had been previously considered absent.Methodology/principal findingsBased on a prospective population study model and demographic characteristics (sex and age), we analyzed the anti-CHIKV IgG seroconversion rate in 341 subjects by ELISA over four years. The seroprevalence increased from 0.35% in the first year to 2.3% after 3 years of follow-up. Additionally, we investigated 497 samples from a blood panel collected from dengue-suspected individuals during the 2019 dengue outbreak in SJdRP. In total, 4.4% were positive for anti-CHIKV IgM, and 8.6% were positive for IgG. To exclude alphavirus cross-reactivity, we evaluated the presence of anti-Mayaro virus (MAYV) IgG by ELISA, and the positivity rate was 0.3% in the population study and 0.8% in the blood panel samples. In CHIKV and MAYV plaque reduction neutralization tests (PRNTs), the positivity rate for CHIKV-neutralizing antibodies in these ELISA-positive samples was 46.7%, while no MAYV-neutralizing antibodies were detected. Genomic sequencing and phylogenetic analysis revealed CHIKV genotype ECSA in São José do Rio Preto, SP. Finally, mosquitoes collected to complement human surveillance revealed CHIKV positivity of 2.76% of A. aegypti and 9.09% of A. albopictus (although it was far less abundant than A. aegypti) by RT-qPCR.Conclusions/significanceOur data suggest cryptic CHIKV circulation in SJdRP detected by continual active surveillance. These low levels, but increasing, of viral circulation highlight the possibility of CHIKV outbreaks, as there is a large naïve population. Improved knowledge of the epidemiological situation might aid in outbreaks prevention.

Abstract Objective To perform a molecular screening to detect infections by the mayaro virus and possible coinfections with Chikungunya during an outbreak in the state of Tocantins/Brazil in 2017. Results Of a total 102 samples analyzed in this study, 6 cases were identified with simultaneous infection between mayaro and chikungunya viruses (5.88%). In these 6 samples, the mean Cycle threshold (Ct) for CHIKV was 26.87 (SD ± 10.54) and for MAYV was 29.58 (SD ± 6.34). The mayaro sequences generated showed 95–100% identity to other Brazilian sequences of this virus and with other MAYV isolates obtained from human and arthropods in different regions of the world. The remaining samples were detected with CHIKV monoinfection (41 cases), DENV monoinfection (50 cases) and coinfection between CHIKV/DENV (5 cases). We did not detect MAYV monoinfections.

Abstract Arboviruses impose a serious threat to public health services. We report a case of a patient returning from a work trip to the Amazon basin with myalgia, arthralgia, fever, and headache. During this travel, the patient visited riverside communities. Both dengue and Chikungunya fevers were first suspected, tested for, and excluded. Mayaro fever was then confirmed by reverse transcription polymerase chain reaction followed by next-generation sequencing and phylogenetic reconstruction. The increased awareness of physicians and consequent detection of Mayaro virus in this case was only possible due a previous surveillance program with specific health personnel training about these neglected arboviruses.

Abstract Background São José do Rio Preto is one of the cities of the state of São Paulo, Brazil, that is hyperendemic for dengue, with the presence of the four dengue serotypes. Objectives: to calculate dengue seroprevalence in a neighbourhood of São José do Rio Preto and identify if socioeconomic and demographic covariates are associated with dengue seropositivity. Methods A cohort study to evaluate dengue seroprevalence and incidence and associated factors on people aged 10 years or older, was assembled in Vila Toninho neighbourhood, São José do Rio Preto. The participant enrolment occurred from October 2015 to March 2016 (the first wave of the cohort study), when blood samples were collected for serological test (ELISA IgG anti-DENV) and questionnaires were administrated on socio-demographic variables. We evaluated the data collected in this first wave using a cross-sectional design. We considered seropositive the participants that were positive in the serological test (seronegative otherwise). We modelled the seroprevalence with a logistic regression in a geostatistical approach. The Bayesian inference was made using integrated nested Laplace approximations (INLA) coupled with the Stochastic Partial Differential Equation method (SPDE). Results We found 986 seropositive individuals for DENV in 1322 individuals surveyed in the study area in the first wave of the cohort study, corresponding to a seroprevalence of 74.6% (95%CI: 72.2–76.9). Between the population that said never had dengue fever, 68.4% (566/828) were dengue seropositive. Older people, non-white and living in a house (instead of in an apartment), were positively associated with dengue seropositivity. We adjusted for the other socioeconomic and demographic covariates, and accounted for residual spatial dependence between observations, which was found to present up to 800 m. Conclusions Only one in four people aged 10 years or older did not have contact with any of the serotypes of dengue virus in Vila Toninho neighbourhood in São José do Rio Preto. Age, race and type of house were associated with the occurrence of the disease. The use of INLA in a geostatistical approach in a Bayesian context allowed us to take into account the spatial dependence between the observations and identify the associated covariates to dengue seroprevalence.